1,427 research outputs found
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Finite-Time State Estimation for Delayed Neural Networks with Redundant Delayed Channels
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61703245 and 61873148); 10.13039/501100010029-Taishan Scholar Project of Shandong Province of China; 10.13039/501100002858-China Post-Doctoral Science Foundation (Grant Number: 2016M600547); Qingdao Post-Doctoral Applied Research Project (Grant Number: 2016117); Post-Doctoral Special Innovation Foundation of Shandong (Grant Number: 201701015); 10.13039/501100000288-Royal Society of the U.K.;
10.13039/100005156-Alexander von Humboldt Foundation of German
Recommended from our members
Maximum Correntropy Filtering for Complex Networks With Uncertain Dynamical Bias: Enabling Componentwise Event-Triggered Transmission
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62203016, U2241214, T2121002 and 61933007);;
10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021TQ0009);
Royal Society, U (Grant Number: 0000DONOTUSETHIS0000.K);
Alexander von Humboldt Foundation of Germany
Exponential Lag Synchronization of Cohen-Grossberg Neural Networks with Discrete and Distributed Delays on Time Scales
In this article, we investigate exponential lag synchronization results for
the Cohen-Grossberg neural networks (C-GNNs) with discrete and distributed
delays on an arbitrary time domain by applying feedback control. We formulate
the problem by using the time scales theory so that the results can be applied
to any uniform or non-uniform time domains. Also, we provide a comparison of
results that shows that obtained results are unified and generalize the
existing results. Mainly, we use the unified matrix-measure theory and Halanay
inequality to establish these results. In the last section, we provide two
simulated examples for different time domains to show the effectiveness and
generality of the obtained analytical results.Comment: 20 pages, 18 figure
Recommended from our members
Simultaneous State and Unknown Input Estimation for Complex Networks With Redundant Channels Under Dynamic Event-Triggered Mechanisms
National Natural Science Foundation of China (Grant Number: 62003121, 61873082, 61873148 and 61933007); Zhejiang Provincial Natural Science Foundation of China (Grant Number: LQ20F030014);
Outstanding Youth Science Foundation of Heilongjiang Province of China (Grant Number: JC2018001);
Fundamental Research Foundation for Universities of Heilongjiang Province of China (Grant Number: 2019-KYYWF-0215); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany
Bidirectional Reactive Programming for Machine Learning
Reactive languages are dedicated to the programming of systems which interact
continuously and concurrently with their environment. Values take the form of
unbounded streams modeling the (discrete) passing of time or the sequence of
concurrent interactions. While conventional reactivity models recurrences
forward in time, we introduce a symmetric reactive construct enabling backward
recurrences. Constraints on the latter allow to make the implementation
practical. Machine Learning (ML) systems provide numerous motivations for all
of this: we demonstrate that reverse-mode automatic differentiation,
backpropagation, batch normalization, bidirectional recurrent neural networks,
training and reinforcement learning algorithms, are all naturally captured as
bidirectional reactive programs
Modeling Events and Interactions through Temporal Processes -- A Survey
In real-world scenario, many phenomena produce a collection of events that
occur in continuous time. Point Processes provide a natural mathematical
framework for modeling these sequences of events. In this survey, we
investigate probabilistic models for modeling event sequences through temporal
processes. We revise the notion of event modeling and provide the mathematical
foundations that characterize the literature on the topic. We define an
ontology to categorize the existing approaches in terms of three families:
simple, marked, and spatio-temporal point processes. For each family, we
systematically review the existing approaches based based on deep learning.
Finally, we analyze the scenarios where the proposed techniques can be used for
addressing prediction and modeling aspects.Comment: Image replacement
A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings
Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich
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